A digital image is basically a two-dimensional array of digital data with each entry representing a pixel of the digitized image. Each pixel data can have a few components like color, e.g. red, green, and blue. A crucial image processing operation is expanding an image by an arbitrary factor and thereby creating an enlarged image. Deinterlacing is an example of such an operation where a video field is enlarged in vertical direction with a 1:2 scale factor. It is well known that in creating an enlarged image from an original image, it is necessary to interpolate between pixels of the original digital data array to achieve a high quality output image. Some prior art methods for image expansion interpolate the horizontal and vertical dimensions of the digitized image separately. These methods generate noticeable artifacts in the expanded images. The worst two types of resulting artifacts are zigzags (also called “jaggies”) and the blurring effects. Most zigzag and blurring effects occur along the edges, substantially affecting the perceived quality of expanded edges.
It is known in the art that edge adaptive interpolation, i.e., interpolation along the edges in a digital image produces better quality results over interpolation across the edges of the image.
U.S. Pat. No. 5,991,664, to Hsu et. al., discloses a system for enhancing the resolution of a video image with classification and adaptive interpolation modules. The classification module generates a dominant orientation parameter for each original image pixel, and the adaptive interpolation module reads the dominant orientation parameter and selects one of the pre-determinant parametric coefficient sets responsive to generate each target image pixel.
U.S. Pat. No. 5,991,463 to Greggain et al. discloses a method of generating an upsampled target pixel, positioned between two lines of input source data. First, the difference in values of pixels of the digital source data in a region surrounding the upsampled target pixel to be generated in a number of directions are calculated and examined to detect an interpolation direction. Then, intermediate pixels between pixels on line segments of the input image are generated based on determined interpolation directions. And then, interpolations between the intermediate pixels are performed to generate the upsampled target pixel.
U.S. Pat. No. 6,133,957 to Campbell discloses an adaptive diagonal interpolation method for image resolution enhancement that consists of interpolation direction detection by analysis of a weighted combination of a vertical direction and a best-choice diagonal direction. An interpolation circuit then generates a target pixel by interpolating neighborhood pixels that are along a direction represented by interpolation direction signal.
U.S. Pat. No. 5,929,918 to Pereira et al. discloses an edge-oriented intra-field/inter-field interpolation filter for improved quality video appliances comprising four circuits. The first circuit detects an image edge. The second circuit uses output from the first circuit to generate a first signal corresponding to an average of the discrete image elements along a direction of the image edge. The third circuit uses output from the first circuit to detect a texture image area and for generating a second signal depending on a degree of existence of the image edge. The fourth circuit generates an output signal by combining the first signal with the third signal in a proportion dependent upon the second signal.
However, these and other prior art systems do not provide a high degree of adaptivity. The edge detection modules in prior art usually generate a direction of an edge but do not provide parameters describing the certainty of the edge, which can control the adaptive filter for better image quality. These systems are not adaptive to a certain edge scale. They usually exploit simple bilinear or bicubic interpolations that restrict the quality of the generated image. Therefore, there is a need for an improved interpolation system and method to support more flexible edge orientation and scale adaptive image enlargement and enhancement.